Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

• Define a Function that Makes a Graph
• Question 1: Use yfinance to Extract Stock Data
• Question 2: Use Webscraping to Extract Tesla Revenue Data
• Question 3: Use yfinance to Extract Stock Data
• Question 4: Use Webscraping to Extract GME Revenue Data
• Question 5: Plot Tesla Stock Graph
• Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

---

Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.

!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly

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import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots 

import plotly.io as pio
pio.renderers.default = "iframe"

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

tesla_data = tesla.history(period="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

tesla_data.reset_index(inplace=True)
tesla_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2010-06-29 00:00:00-04:00	1.266667	1.666667	1.169333	1.592667	281494500	0.0	0.0
1	2010-06-30 00:00:00-04:00	1.719333	2.028000	1.553333	1.588667	257806500	0.0	0.0
2	2010-07-01 00:00:00-04:00	1.666667	1.728000	1.351333	1.464000	123282000	0.0	0.0
3	2010-07-02 00:00:00-04:00	1.533333	1.540000	1.247333	1.280000	77097000	0.0	0.0
4	2010-07-06 00:00:00-04:00	1.333333	1.333333	1.055333	1.074000	103003500	0.0	0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

from bs4 import BeautifulSoup

# Parse the HTML data using BeautifulSoup
# You can use either 'html.parser' or 'html5lib' as the parser
soup = BeautifulSoup(html_data, 'html.parser')  # or 'html5lib'

# Now `soup` is a BeautifulSoup object that represents the parsed HTML
# You can use it to navigate and search the HTML structure
#print(soup.prettify())  # Print the prettified HTML structure

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions

Here are the step-by-step instructions:

1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame

<details><summary>Click here if you need help locating the table</summary> ``` Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab soup.find_all("tbody")[1] If you want to use the read_html function the table is located at index 1 We are focusing on quarterly revenue in the lab. ``` </details>

from bs4 import BeautifulSoup
import pandas as pd

# Parse the HTML data using BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')

# Locate the second <tbody> tag (index 1)
tbody = soup.find_all("tbody")[1]

# Extract all rows from the <tbody>
rows = tbody.find_all("tr")

# Initialize lists to store the data
dates = []
revenues = []

# Loop through each row and extract the data
for row in rows:
    # Extract all <td> elements in the row
    cols = row.find_all("td")
    
    # Ensure there are at least 2 columns (Date and Revenue)
    if len(cols) >= 2:
        date = cols[0].text.strip()  # First column is Date
        revenue = cols[1].text.strip()  # Second column is Revenue
        
        # Append the data to the lists
        dates.append(date)
        revenues.append(revenue)

# Create a DataFrame
tesla_revenue = pd.DataFrame({
    "Date": dates,
    "Revenue": revenues
})

# Display the DataFrame
print(tesla_revenue)

          Date  Revenue
0   2022-09-30  $21,454
1   2022-06-30  $16,934
2   2022-03-31  $18,756
3   2021-12-31  $17,719
4   2021-09-30  $13,757
5   2021-06-30  $11,958
6   2021-03-31  $10,389
7   2020-12-31  $10,744
8   2020-09-30   $8,771
9   2020-06-30   $6,036
10  2020-03-31   $5,985
11  2019-12-31   $7,384
12  2019-09-30   $6,303
13  2019-06-30   $6,350
14  2019-03-31   $4,541
15  2018-12-31   $7,226
16  2018-09-30   $6,824
17  2018-06-30   $4,002
18  2018-03-31   $3,409
19  2017-12-31   $3,288
20  2017-09-30   $2,985
21  2017-06-30   $2,790
22  2017-03-31   $2,696
23  2016-12-31   $2,285
24  2016-09-30   $2,298
25  2016-06-30   $1,270
26  2016-03-31   $1,147
27  2015-12-31   $1,214
28  2015-09-30     $937
29  2015-06-30     $955
30  2015-03-31     $940
31  2014-12-31     $957
32  2014-09-30     $852
33  2014-06-30     $769
34  2014-03-31     $621
35  2013-12-31     $615
36  2013-09-30     $431
37  2013-06-30     $405
38  2013-03-31     $562
39  2012-12-31     $306
40  2012-09-30      $50
41  2012-06-30      $27
42  2012-03-31      $30
43  2011-12-31      $39
44  2011-09-30      $58
45  2011-06-30      $58
46  2011-03-31      $49
47  2010-12-31      $36
48  2010-09-30      $31
49  2010-06-30      $28
50  2010-03-31      $21
51  2009-12-31         
52  2009-09-30      $46
53  2009-06-30      $27

Execute the following line to remove the comma and dollar sign from the Revenue column.

tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

Execute the following lines to remove an null or empty strings in the Revenue column.

tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

tesla_revenue.tail()

	Date	Revenue
48	2010-09-30	$31
49	2010-06-30	$28
50	2010-03-31	$21
52	2009-09-30	$46
53	2009-06-30	$27

Question 3: Use yfinance to Extract Stock Data

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

gme = yf.Ticker("GME")

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

gme_data=gme.history(period="max")

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

gme_data.reset_index(inplace=True)
gme_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2002-02-13 00:00:00-05:00	1.620128	1.693350	1.603296	1.691666	76216000	0.0	0.0
1	2002-02-14 00:00:00-05:00	1.712707	1.716074	1.670626	1.683250	11021600	0.0	0.0
2	2002-02-15 00:00:00-05:00	1.683251	1.687459	1.658002	1.674834	8389600	0.0	0.0
3	2002-02-19 00:00:00-05:00	1.666418	1.666418	1.578048	1.607504	7410400	0.0	0.0
4	2002-02-20 00:00:00-05:00	1.615920	1.662210	1.603296	1.662210	6892800	0.0	0.0

Question 4: Use Webscraping to Extract GME Revenue Data

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
response = requests.get(url)
html_data_2 = response.text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

import requests
from bs4 import BeautifulSoup
import pandas as pd

soup = BeautifulSoup(html_data,"html.parser")

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table

    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

import requests
from bs4 import BeautifulSoup
import pandas as pd

gme_table = soup.find_all("tbody")[1]  # Adjust index if necessary

# Extract data from table
gme_revenue = []
for row in gme_table.find_all("tr"):
    cols = row.find_all("td")
    if len(cols) > 1:
        date = cols[0].text.strip()
        revenue = cols[1].text.strip()
        gme_revenue.append([date, revenue])

# Convert to DataFrame
gme_revenue = pd.DataFrame(gme_revenue, columns=["Date", "Revenue"])
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace("$", "").str.replace(",", "").astype(float)
print(gme_revenue.head())

         Date  Revenue
0  2020-04-30   1021.0
1  2020-01-31   2194.0
2  2019-10-31   1439.0
3  2019-07-31   1286.0
4  2019-04-30   1548.0

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

gme_revenue.tail()

	Date	Revenue
57	2006-01-31	1667.0
58	2005-10-31	534.0
59	2005-07-31	416.0
60	2005-04-30	475.0
61	2005-01-31	709.0

Question 5: Plot Tesla Stock Graph

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

Hint

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.

import plotly.graph_objects as go
from plotly.subplots import make_subplots
from IPython.display import display, HTML

# Convert the Revenue column to string type
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].astype(str)

# Clean the Revenue column by removing dollar signs and commas
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace("$", "", regex=False).str.replace(",", "", regex=False)

# Convert the Revenue column to float
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].astype(float)

# Display the cleaned DataFrame
print(tesla_revenue)

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

make_graph(tesla_data, tesla_revenue, 'Tesla Stock Data Graph')

          Date  Revenue
0   2022-09-30  21454.0
1   2022-06-30  16934.0
2   2022-03-31  18756.0
3   2021-12-31  17719.0
4   2021-09-30  13757.0
5   2021-06-30  11958.0
6   2021-03-31  10389.0
7   2020-12-31  10744.0
8   2020-09-30   8771.0
9   2020-06-30   6036.0
10  2020-03-31   5985.0
11  2019-12-31   7384.0
12  2019-09-30   6303.0
13  2019-06-30   6350.0
14  2019-03-31   4541.0
15  2018-12-31   7226.0
16  2018-09-30   6824.0
17  2018-06-30   4002.0
18  2018-03-31   3409.0
19  2017-12-31   3288.0
20  2017-09-30   2985.0
21  2017-06-30   2790.0
22  2017-03-31   2696.0
23  2016-12-31   2285.0
24  2016-09-30   2298.0
25  2016-06-30   1270.0
26  2016-03-31   1147.0
27  2015-12-31   1214.0
28  2015-09-30    937.0
29  2015-06-30    955.0
30  2015-03-31    940.0
31  2014-12-31    957.0
32  2014-09-30    852.0
33  2014-06-30    769.0
34  2014-03-31    621.0
35  2013-12-31    615.0
36  2013-09-30    431.0
37  2013-06-30    405.0
38  2013-03-31    562.0
39  2012-12-31    306.0
40  2012-09-30     50.0
41  2012-06-30     27.0
42  2012-03-31     30.0
43  2011-12-31     39.0
44  2011-09-30     58.0
45  2011-06-30     58.0
46  2011-03-31     49.0
47  2010-12-31     36.0
48  2010-09-30     31.0
49  2010-06-30     28.0
50  2010-03-31     21.0
52  2009-09-30     46.0
53  2009-06-30     27.0

/tmp/ipykernel_696/3139380720.py:21: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipykernel_696/3139380720.py:22: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

Question 6: Plot GameStop Stock Graph

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`

import plotly.graph_objects as go
from plotly.subplots import make_subplots
from IPython.display import display, HTML

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

make_graph(gme_data, gme_revenue, 'GME Stock Data Graph')

/tmp/ipykernel_696/473891659.py:9: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipykernel_696/473891659.py:10: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log

Date (YYYY-MM-DD)	Version	Changed By	Change Description
2022-02-28	1.2	Lakshmi Holla	Changed the URL of GameStop
2020-11-10	1.1	Malika Singla	Deleted the Optional part
2020-08-27	1.0	Malika Singla	Added lab to GitLab

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